标题： CLaP -- 时间序列的状态检测 显示英文标题

标题： CLaP -- State Detection from Time Series

摘要： 来自机器、智能设备和环境的传感器数据量不断增加，导致大量高分辨率且未标注的时间序列（TS）的出现。这些记录编码了可以从物理现象中抽象建模的潜在状态及其转换的可识别属性。 这些状态及其转换的无监督定位和识别是时间序列状态检测（TSSD）的任务。我们引入了CLaP，这是一种新的、高度准确且高效的TSSD算法。它通过应用新颖的自监督技术来利用时间序列分类的预测能力，在无监督设置下实现TSSD。为此，CLaP 使用带有分段标记子序列的分类器进行交叉验证，以量化分段之间的混淆。如果合并具有高混淆度的分段标签（表示相同的潜在状态）能够提高整体分类质量，则执行此操作。我们使用来自四个基准的391个TS进行了实验评估，发现CLaP比五种最先进的竞争者在检测状态方面显著更精确。它实现了最佳的准确率-运行时权衡，并且可以扩展到大型时间序列。我们提供了CLaP的Python实现，该实现可以在时间序列分析工作流中部署。 显示英文摘要

摘要： The ever-growing amount of sensor data from machines, smart devices, and the environment leads to an abundance of high-resolution, unannotated time series (TS). These recordings encode the recognizable properties of latent states and transitions from physical phenomena that can be modelled as abstract processes. The unsupervised localization and identification of these states and their transitions is the task of time series state detection (TSSD). We introduce CLaP, a new, highly accurate and efficient algorithm for TSSD. It leverages the predictive power of time series classification for TSSD in an unsupervised setting by applying novel self-supervision techniques to detect whether data segments emerge from the same state or not. To this end, CLaP cross-validates a classifier with segment-labelled subsequences to quantify confusion between segments. It merges labels from segments with high confusion, representing the same latent state, if this leads to an increase in overall classification quality. We conducted an experimental evaluation using 391 TS from four benchmarks and found CLaP to be significantly more precise in detecting states than five state-of-the-art competitors. It achieves the best accuracy-runtime tradeoff and is scalable to large TS. We provide a Python implementation of CLaP, which can be deployed in TS analysis workflows.